LLM Invariant Synthesis for Scalable Program Validation
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Solution Overview
Problem
Current methods for validating software and hardware logic are inefficient due to the difficulty in automatically synthesizing invariants, especially for complex code structures like 'when' statements, 'if' statements, and loops, which limits scalability and requires manual human intervention.
Innovation Solution
The system automates the synthesis of invariants using a Large Language Model (LLM) that generates and tests candidate invariants, incorporating contextual data to produce context-rich model inputs, and scores them based on weighted subscores, with counterexample checking and proof testing to ensure validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for synthesizing invariants, then accuracy can be maintained, but productivity is low and the process does not scale
Solution Approach 1:
The system enables invariants to be synthesized automatically through the LLM-based apparatus, which self-manages the generation, scoring, and validation processes without requiring manual human intervention for each invariant, thereby dramatically improving productivity while maintaining accuracy
Solution Approach 2:
The patent replaces manual mechanical processes of invariant synthesis with an automated LLM-based system that uses machine learning models to generate and validate invariants programmatically, enabling scalable automation while preserving the quality standards previously maintained only through manual methods
2Productivity
If legacy techniques are used for invariant synthesis, then simplicity is maintained, but the scaling complexity grows exponentially with input model size
Solution Approach 1:
The patent introduces a scoring mechanism with weighted subscores that transforms the invariant validation process from exponential complexity to linear scaling by changing the evaluation parameters - instead of exhaustive checking, the system uses prioritized scoring thresholds to efficiently identify and validate the most promising invariants first
Solution Approach 2:
The system performs preliminary scoring and filtering of candidate invariants before full validation, using the LLM to pre-assess quality metrics and prioritize which invariants warrant detailed verification, thereby reducing the overall computational burden and enabling linear scaling with input size
3Reliability
If comprehensive testing is performed on all candidate invariants, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by implementing a tiered validation approach where not all candidate invariants undergo complete rigorous testing - instead, the LLM scores candidates and only those meeting certain thresholds receive full validation, while lower-priority candidates receive streamlined checking, thereby maintaining reliability for critical invariants while reducing overall validation time
Solution Approach 2:
The scoring mechanism provides feedback on candidate invariant quality, allowing the system to adaptively adjust validation depth based on preliminary assessments - high-scoring invariants receive comprehensive testing while low-scoring ones are quickly filtered or given minimal validation, optimizing the balance between reliability and time efficiency
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed. An example apparatus to automate invariant synthesis includes: interface circuitry; instructions; and at least one programmable circuit to be programmed by the instructions to: produce a model input based on a program and/or contextual data corresponding to the program; provide the model input to a Large Language Model (LLM), the LLM to produce an invariant based on the model input; score the invariant; and incorporate the invariant into the program based on the score.